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specs/017-llm-analysis-plugin/plan.md

@@ -23,6 +23,25 @@ This feature implements two new plugins for the `ss-tools` platform: `DashboardV
 **Constraints**: Must integrate with existing `PluginBase` and `TaskManager`. Secure storage for API keys.
 **Scale/Scope**: Support for configurable LLM providers, scheduled tasks, and notification integration.
 
+## Implementation Notes
+
+### Screenshot Capture Implementation
+The screenshot service uses Playwright with Chrome DevTools Protocol (CDP) to avoid font loading timeouts in headless mode. Key implementation details:
+- **Full Page Capture**: Uses CDP `Page.captureScreenshot` with `captureBeyondViewport: true` and `fromSurface: true`
+- **Tab Switching**: Implements recursive tab switching to trigger lazy-loaded chart rendering on multi-tab dashboards
+- **Authentication**: Uses direct UI login flow (navigating to `/login/` and filling credentials) instead of API cookie injection for better reliability
+- **Resolution**: 1920px width with dynamic full page height calculation
+
+### Authentication Flow
+The service authenticates via Playwright UI login rather than API authentication:
+1. Navigate to `/login/` page
+2. Fill username/password fields (supports multiple Superset versions)
+3. Click login button
+4. Verify successful redirect to `/superset/welcome/`
+5. Navigate to dashboard with valid session
+
+This approach is more reliable than API-to-UI cookie injection which was causing 403 Forbidden errors.
+
 ## Constitution Check
 
 *GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*
@@ -60,7 +79,8 @@ backend/
 │   │   ├── llm_analysis/       # New Plugin Directory
 │   │   │   ├── __init__.py
 │   │   │   ├── plugin.py       # Implements DashboardValidationPlugin & DocumentationPlugin
-│   │   │   ├── service.py      # LLM interaction logic
+│   │   │   ├── service.py      # LLM interaction logic + ScreenshotService (CDP, tab switching)
+│   │   │   ├── scheduler.py    # Scheduled validation task handler
 │   │   │   └── models.py       # Pydantic models for LLM config/results
 │   │   └── git/                # Existing Git Plugin
 │   │       └── ...             # Update for commit message generation